pentagi vs ifixai
Two of the top projects in Security, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
pentagi
Autonomous multi-agent penetration testing system with sandboxed security tools
ifixai
Python CLI that audits AI agents for policy, access and reliability failures
Verdict
pentagi ranks higher, 64 to 51. Of the 3 score parts we measure for both, pentagi leads on 2, ifixai on none and 1 is even.
- Adoptionpentagi leads by 17 points, 36 to 19: a wide gap.
- FreshnessEven at 100.
- Agent-readypentagi leads by 40 points, 40 to 0: a wide gap.
Pick pentagi if you are
Security engineers, pentesters and red teams doing authorized testing
- Runs 20+ pentest tools (nmap, Metasploit, sqlmap) in an isolated Docker sandbox
- Supports 10+ LLM providers, custom OpenAI-compatible endpoints, and local Ollama or vLLM
Pick ifixai if you are
Teams testing deployed AI agents for policy and access-control failures
- Tests a real agent over its HTTP endpoint, not only a bare model API
- Default 10-check suite ends in a grade; --full-run covers all 60 inspections
| What we compare | pentagi | ifixai |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 36, known | 19, niche |
| Freshness | 100, active | 100, active |
| Maintenance | Not scored for this kind | Not scored for this kind |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 40, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 25.3k | 24.2k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Go | Python |
| Docker | Yes | No |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
pentagi
PentAGI takes a target and goal in plain language, then uses a team of specialized agents (orchestrator, researcher, developer, executor) to plan and run 20+ tools such as nmap, Metasploit and sqlmap inside an isolated Docker sandbox. Findings are stored in PostgreSQL with pgvector, with an optional Neo4j/Graphiti knowledge graph, and it produces vulnerability reports. It works with 10+ LLM providers, any OpenAI-compatible endpoint, or local models via Ollama or vLLM.
Who it is for: Security engineers, pentesters and red teams doing authorized testing
Strengths
- Runs 20+ pentest tools (nmap, Metasploit, sqlmap) in an isolated Docker sandbox
- Supports 10+ LLM providers, custom OpenAI-compatible endpoints, and local Ollama or vLLM
- Persistent memory in PostgreSQL with pgvector, optional Neo4j knowledge graph via Graphiti
- REST and GraphQL APIs with Bearer tokens; Langfuse, Grafana and Prometheus integrations
Weaknesses
- Full stack spans many containers (Postgres, Neo4j, Langfuse, ClickHouse, Grafana), heavy to self-host
- Agent profiles, plugins, MCP servers and attack-chain view are Enterprise-only
- Not a BAS or adversary emulation tool; no predefined campaigns
- Report export limited to web, Markdown and PDF; no JSON export
- GPU optional
- Docker + Compose
- Needs Docker, Docker Compose, PostgreSQL with pgvector, Neo4j (optional), Langfuse (optional), Grafana/Prometheus (optional)
- Models: OpenAI, Anthropic, Google Gemini, AWS Bedrock, Ollama
ifixai
iFixAi runs up to 60 inspections against a model or a deployed agent's HTTP endpoint and grades the answers with a separate judge model. The default run covers 10 checks, such as role-based tool access, prompt injection, privilege escalation and data leakage between users. It writes JSON and Markdown reports plus a terminal scorecard, and also ships as a Claude Code and Codex plugin and a skill for other agents.
Who it is for: Teams testing deployed AI agents for policy and access-control failures
Strengths
- Tests a real agent over its HTTP endpoint, not only a bare model API
- Default 10-check suite ends in a grade; --full-run covers all 60 inspections
- Citable grades use a judge from a different vendor than the system under test
- Runs from CLI, CI flags, a Claude Code/Codex plugin, or a skill for other editors
Weaknesses
- A citable grade needs two API keys from different vendors
- Full-suite judging costs roughly $10-18 per run, plus the agent's own bill
- Bundled default fixture has seeded defects, so a custom fixture is needed for real grades
- Full mode requires a hand-built fixture
- no GPU
- Needs Python 3.10+, LLM provider API keys (system under test and judge), uv (for skill install)
- Models: OpenAI, Anthropic, Gemini, Azure, Bedrock
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